Pharmaceutical Research

, Volume 32, Issue 10, pp 3159–3169

Influence of the Size of Cohorts in Adaptive Design for Nonlinear Mixed Effects Models: An Evaluation by Simulation for a Pharmacokinetic and Pharmacodynamic Model for a Biomarker in Oncology

Research Paper

DOI: 10.1007/s11095-015-1693-3

Cite this article as:
Lestini, G., Dumont, C. & Mentré, F. Pharm Res (2015) 32: 3159. doi:10.1007/s11095-015-1693-3

Abstract

Purpose

In this study we aimed to evaluate adaptive designs (ADs) by clinical trial simulation for a pharmacokinetic-pharmacodynamic model in oncology and to compare them with one-stage designs, i.e., when no adaptation is performed, using wrong prior parameters.

Methods

We evaluated two one-stage designs, ξ0 and ξ*, optimised for prior and true population parameters, Ψ0 and Ψ*, and several ADs (two-, three- and five-stage). All designs had 50 patients. For ADs, the first cohort design was ξ0. The next cohort design was optimised using prior information updated from the previous cohort. Optimal design was based on the determinant of the Fisher information matrix using PFIM. Design evaluation was performed by clinical trial simulations using data simulated from Ψ*.

Results

Estimation results of two-stage ADs and ξ* were close and much better than those obtained with ξ0. The balanced two-stage AD performed better than two-stage ADs with different cohort sizes. Three- and five-stage ADs were better than two-stage with small first cohort, but not better than the balanced two-stage design.

Conclusions

Two-stage ADs are useful when prior parameters are unreliable. In case of small first cohort, more adaptations are needed but these designs are complex to implement.

KEY WORDS

adaptive design Fisher information matrix nonlinear mixed effects model optimal design pharmacokinetic-pharmacodynamic 

ABBREVIATIONS

AD

Adaptive design

FIM

Fisher information matrix

NLMEM

Nonlinear mixed effects model

PD

Pharmacodynamic

PK

Pharmacokinetic

REE

Relative estimation error

RRMSE

Relative root mean squared error

TGF-β

Transforming growth factor β

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Giulia Lestini
    • 1
  • Cyrielle Dumont
    • 1
  • France Mentré
    • 1
  1. 1.IAME, UMR 1137, INSERMUniversité Paris DiderotParisFrance

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